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Random assignment of participants helps to ensure that any differences between and within the groups are not systematic at the outset of the experiment.
Mathematically, there are distinctions between randomization, pseudorandomization, and quasirandomization, as well as between random number generators and pseudorandom number generators.
How much these differences matter in experiments (such as clinical trials) is a matter of trial design and statistical rigor, which affect evidence grading.
people who arrive early versus people who arrive late.
Imagine the experimenter instead uses a coin flip to randomly assign participants.
One way of designing the study would be to select a sample of people and divide them into a control group (i.e., those who don't have an apple a day) and a treatment group (i.e., those who do have an apple a day). The best way is to do it randomly in order to cancel out the idiosyncrasies of your subject pool.
Imagine if you decided to choose the groups based on cholesterol intake. Since cholesterol affects blood pressure, you as an experimenter would not know if the changes in health were due to the apple a day or the amount of cholesterol intake.To express this same idea statistically - If a randomly assigned group is compared to the mean it may be discovered that they differ, even though they were assigned from the same group.If a test of statistical significance is applied to randomly assigned groups to test the difference between sample means against the null hypothesis that they are equal to the same population mean (i.e., population mean of differences = 0), given the probability distribution, the null hypothesis will sometimes be "rejected," that is, deemed not plausible.That is, the groups will be sufficiently different on the variable tested to conclude statistically that they did not come from the same population, even though, procedurally, they were assigned from the same total group.For example, using random assignment may create an assignment to groups that has 20 blue-eyed people and 5 brown-eyed people in one group.Random assignment or random placement is an experimental technique for assigning human participants or animal subjects to different groups in an experiment (e.g., a treatment group versus a control group) using randomization, such as by a chance procedure (e.g., flipping a coin) or a random number generator.This ensures that each participant or subject has an equal chance of being placed in any group.Because most basic statistical tests require the hypothesis of an independent randomly sampled population, random assignment is the desired assignment method because it provides control for all attributes of the members of the samples—in contrast to matching on only one or more variables—and provides the mathematical basis for estimating the likelihood of group equivalence for characteristics one is interested in, both for pretreatment checks on equivalence and the evaluation of post treatment results using inferential statistics.More advanced statistical modeling can be used to adapt the inference to the sampling method.The thinking behind random assignment is that by randomizing treatment assignment, then the group attributes for the different treatments will be roughly equivalent and therefore any effect observed between treatment groups can be linked to the treatment effect and is not a characteristic of the individuals in the group.In experimental design, random assignment of participants in experiments or treatment and control groups help to ensure that any differences between and within the groups are not systematic at the outset of the experiment.